Data-driven prediction models of photovoltaic energy for smart grid applications

Souabi, Sonia; Chakir, Asmae; Tabaa, Mohamed

doi:10.1016/j.egyr.2023.05.237

Cited by 12 publications

References 35 publications

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Contribution of Green Roof in Urban Energy Saving Project

Qin,

Zou,

Jiang

et al. 2024

Novel Technology and Whole-Process Management in Prefabricated Building

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In this paper, through the simulation of whether the roof greening is arranged on the top of buildings when different weather events occur in summer, it is found that roof greening can produce a local cooling effect of 0.8 ℃–1.5 ℃ and an overall cooling effect of 0.6 ℃–1.3 ℃ in summer, which has a good positive impact on urban energy supply and stability. Through energy consumption simulation, it is found that the arrangement of green roof can produce 3 W/m2/h cooling effect on the room, and the entire cooling season can reduce the simulated building energy consumption of 21772.8 KWH. It provides relevant data for the restoration of urban ecological environment and the follow-up study of urban energy conservation.

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Contribution of Green Roof in Urban Energy Saving Project

Qin,

Zou,

Jiang

et al. 2024

Novel Technology and Whole-Process Management in Prefabricated Building

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NeuroQuMan: quantum neural network-based consumer reaction time demand response predictive management

Safari,

Badamchizadeh

2024

Neural Comput & Applic

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Temporal forecasting by converting stochastic behaviour into a stable pattern in electric grid

Qashou,

Yousef,

Hazzaa

et al. 2024

Int J Syst Assur Eng Manag

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The malfunction variables of power stations are related to the areas of weather, physical structure, control, and load behavior. To predict temporal power failure is difficult due to their unpredictable characteristics. As high accuracy is normally required, the estimation of failures of short-term temporal prediction is highly difficult. This study presents a method for converting stochastic behavior into a stable pattern, which can subsequently be used in a short-term estimator. For this conversion, K-means clustering is employed, followed by long-short-term memory and gated recurrent unit algorithms are used to perform the short-term estimation. The environment, the operation, and the generated signal factors are all simulated using mathematical models. Weather parameters and load samples have been collected as part of a dataset. Monte-Carlo simulation using MATLAB programming has been used to conduct experimental estimation of failures. The estimated failures of the experiment are then compared with the actual system temporal failures and found to be in good match. Therefore, to address the gap in knowledge for any future power grid estimated failures, the achieved results in this paper form good basis for a testbed to estimate any grid future failures.

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Data-driven prediction models of photovoltaic energy for smart grid applications

Cited by 12 publications

References 35 publications

Contribution of Green Roof in Urban Energy Saving Project

Contribution of Green Roof in Urban Energy Saving Project

NeuroQuMan: quantum neural network-based consumer reaction time demand response predictive management

Temporal forecasting by converting stochastic behaviour into a stable pattern in electric grid

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